THanks Michael for this.This is much appreciated. So, how can I estimate
the sensitivity and specificity after having the prediction on testing
data. Any thoughts?
Kind regards,
Greg
On Mon, Oct 24, 2022 at 12:10 PM Michael Dewey <li...@dewey.myzen.co.uk>
wrote:
> So predict is a one-dimensional vector of predictions but you are
> treating it as a two-dimensional matrix (presumably you think those are
> the totals).
>
> Michael
>
> On 24/10/2022 16:50, greg holly wrote:
> > Hi Michael,
> >
> > I appreciate your writing. Here are what I have after;
> >
> > > predict_testing <- ifelse(predict > 0.5,1,0)
> > >
> > > head(predict)
> > 1 2 3 5 7 8
> > 0.29006984 0.28370507 0.10761993 0.02204224 0.12873872 0.08127920
> > >
> > > # Sensitivity and Specificity
> > >
> > >
> >
> sensitivity<-(predict_testing[2,2]/(predict_testing[2,2]+predict_testing[2,1]))*100
> > Error in predict_testing[2, 2] : incorrect number of dimensions
> > > sensitivity
> > function (data, ...)
> > {
> > UseMethod("sensitivity")
> > }
> > <bytecode: 0x000002082a2f01d8>
> > <environment: namespace:caret>
> > >
> > >
> >
> specificity<-(predict_testing[1,1]/(predict_testing[1,1]+predict_testing[1,2]))*100
> > Error in predict_testing[1, 1] : incorrect number of dimensions
> > > specificity
> > function (data, ...)
> > {
> > UseMethod("specificity")
> > }
> > <bytecode: 0x000002082a2fa600>
> > <environment: namespace:caret>
> >
> > On Mon, Oct 24, 2022 at 10:45 AM Michael Dewey <li...@dewey.myzen.co.uk
> > <mailto:li...@dewey.myzen.co.uk>> wrote:
> >
> > Rather hard to know without seeing what output you expected and what
> > error message you got if any but did you mean to summarise your
> > variable
> > predict before doing anything with it?
> >
> > Michael
> >
> > On 24/10/2022 16:17, greg holly wrote:
> > > Hi all R-Help ,
> > >
> > > After partitioning my data to testing and training (please see
> > below), I
> > > need to estimate the Sensitivity and Specificity. I failed. It
> > would be
> > > appropriate to get your help.
> > >
> > > Best regards,
> > > Greg
> > >
> > >
> > > inTrain <- createDataPartition(y=data$case,
> > > p=0.7,
> > > list=FALSE)
> > > training <- data[ inTrain,]
> > > testing <- data[-inTrain,]
> > >
> > > attach(training)
> > > #model training and prediction
> > > data_training <- glm(case ~ age+BMI+Calcium+Albumin+meno_1, data =
> > > training, family = binomial(link="logit"))
> > >
> > > predict <- predict(data_training, data_predict = testing, type =
> > "response")
> > >
> > > predict_testing <- ifelse(predict > 0.5,1,0)
> > >
> > > # Sensitivity and Specificity
> > >
> > >
> >
>
> sensitivity<-(predict_testing[2,2]/(predict_testing[2,2]+predict_testing[2,1]))*100
> > > sensitivity
> > >
> > >
> >
>
> specificity<-(predict_testing[1,1]/(predict_testing[1,1]+predict_testing[1,2]))*100
> > > specificity
> > >
> > > [[alternative HTML version deleted]]
> > >
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> >
> > --
> > Michael
> > http://www.dewey.myzen.co.uk/home.html
> > <http://www.dewey.myzen.co.uk/home.html>
> >
> >
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> --
> Michael
> http://www.dewey.myzen.co.uk/home.html
>
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